ClawForge

Fetch, categorize, and summarize GitHub Trending projects across daily, weekly, and monthly spans. Use when a user asks for "trending projects", "latest hot repos", or a "summary of GitHub trends" to provide a structured, categorised report with full hyperlinking. Supports Markdown, HTML (Pinterest design), or both output formats, with optional Obsidian vault storage.

Turn a local folder or GitHub repository into an interactive browser-based course that explains how the codebase works for non-expert programmers and AI-assisted builders. Use when a user asks to make a course, tutorial, walkthrough, learning guide, codebase explanation, or interactive lesson from a project.

Converts a PRD or broad technical requirement into an AI-executable agile workflow with cards, checklists, branch-diff mapping, review gates, and a final comparison report. Use when the user wants PRD review, agile card decomposition, AI execution tracking, implementation branch review, or PRD-to-code traceability.

Two complementary Bezos heuristics in one skill. (A) Two-Way Door — classify decisions by reversibility; reversible = decide fast with 70% info, irreversible = decide slow with 90% info. (B) Regret Minimization — for life-defining choices, project to age 80 and pick what minimizes regret. Use Two-Way Door triggers on Chinese 决策瘫痪 / 反复纠结小事 / 开了三次会还没定 / 大事小事一样慢; Regret Min triggers on 离职 / 创业 / 移民 / 结婚 / 生育 / 转行 / 重大决定 / 这辈子. Especially when team applies identical heavy process to all decisions (need Two-Way), or when user faces once-in-a-decade pivot where rational analysis ties (need Regret Min). Do NOT misclassify One-Way as Two-Way (most expensive mistake), use Regret Min for daily decisions (age-80 view on "what to eat" is meaningless), or run Regret Min in heated emotion (cool 24-48h first to avoid romanticizing risk).

Use BEFORE selecting any other decision framework — Cynefin (kuh-NEV-in) classifies WHICH framework fits the current situation across 5 domains (Clear / Complicated / Complex / Chaotic / Confused). Triggers when user is about to apply a method and the fit feels off, asks meta-questions like "should we follow SOP or explore?", or when same approach that worked last time seems wrong now. Also use after failure when "the method was right but the situation didn't match", or when team argues over deterministic-plan-vs-experimentation. Especially valuable at the start of major decisions to avoid using the wrong hammer for the nail. Do NOT use for trivial classified decisions (don't run Cynefin for "what to eat for lunch"), true Chaotic situations needing immediate stabilizing action (act first, classify later), or teams unfamiliar with the model (use simpler known/unknown binary).

Use when user reports overwhelm from too many tasks, asks for week/sprint/OKR planning help, or describes spending 'all day firefighting' while long-term projects stall. Triggers on Chinese phrases 太多事 / 不知道先做哪个 / 排不过来 / 周计划 / sprint planning / 救火 / 优先级 / overwhelm / 焦虑 / 做不完 / todo 列表炸了, and explicit task triage requests. Especially valuable when waiting list has 15+ pending items, or when user says "重要的事一直推不动 / 都在做紧急的事". Do NOT use for small lists (<5 items just sort by deadline), team-level responsibility assignment (use RAPID/DACI from backlog), or when importance needs quantitative weighting (use weighted decision matrix).

Use when user is stuck by industry conventions ("大家都这么做"), faces a new problem with no precedent, or finds current solutions arbitrarily expensive (intuition says "it shouldn't cost this much"). Triggers on Chinese phrases 行业惯例 / 大家都这么做 / 为什么不能 / 这么贵不合理 / 颠覆性 / 从零开始, and English signals "everyone does it this way", "why is it so expensive", "let's think from scratch". Decompose to irreducible physical/logical truths then rebuild — bypasses analogy thinking. Musk's secret. Do NOT use for problems with proven best practice (Cynefin Clear domain — use SOP), time-critical decisions (first principles takes hours-days), high-emotion contexts (cool down first), or in low-psychological-safety teams (will create conflict with authority).

Use for incident/bug root-cause analysis, postmortem, recurring-problem diagnosis, or process improvement. Triggers on Chinese phrases 复盘 / 根因 / postmortem / 又坏了 / 老是 / 重复出现 / 这不是第一次 / 为啥总是 / RCA, and English signals "this keeps happening", "RCA", "post-mortem", "incident review". Ask "why?" 3-7 times (5 is average not rule) until hit a process/system/design root that can be changed — not the symptom. Do NOT use for one-off random events (could be noise — observe first), Cynefin Complex-domain problems (5 Whys assumes linear causality, complex systems have multi-cause emergence), teams in blame-mode (Why will become 因为 X 不靠谱 chain), or truly unknowable causes (ML model outputs — use statistics not 5 Whys).

Use when user pursues an abstract goal where the path to success is unclear but the path to failure is concrete — '想做出好产品' / '想长寿' / '想升 staff' / '战略方向' / '怎么变得更好'. Triggers Munger's "invert, always invert" mental model: ask 'how do I GUARANTEE failure?' then avoid those. Also use when user is stuck brainstorming positive actions ('should I do X or Y'), facing analysis paralysis on abstract criteria, or when industry advice gives contradictory positive recommendations. Especially powerful for investment decisions, hiring criteria, product PRDs, strategic direction. Do NOT use for concrete quantifiable goals (use OKR), low-cost trial decisions (just try), or in teams already in self-blame mode (will be heard as criticism).

Use when decisions must happen under time pressure or in adversarial/changing conditions — production incidents (P0, on-call, live debugging, rollback choices), competitive moves, real-time negotiations, anything where the situation reacts to your action. Triggers on Chinese phrases 事故 / P0 / 故障 / 排障 / oncall / 救火 / 谈判 / 实时 / 紧急 / 来不及, and English signals "incident", "outage", "live", "real-time", "the other side just". Especially when information is arriving while you must respond, when 5-minute decision cycles beat 1-hour ones, or when you catch yourself wanting to "analyze more" while the situation degrades. Do NOT use for slow life decisions (use wrap), for problems where information is already complete (use eisenhower-matrix or weighted decision matrix), or for Cynefin Clear-domain SOPs.

Use BEFORE committing to a plan, project, hire, launch, funding round, or any irreversible decision. Triggers on Chinese phrases 即将启动 / 上线前 / 发布前 / kick off / 拍板 / 决定要做 / 万事俱备 / 一切都准备好了, and English "before we launch", "ready to ship", "let's commit to". Especially when the team is in a "this is definitely going to work" high-confidence mode (the most dangerous moment), or when user has vague unease they can't articulate. Pre-mortem assumes the plan has already failed 12 months from now, then asks 'why?' — releases concerns silenced by group enthusiasm. Do NOT use after the decision is already irreversible (do post-mortem prep instead), on low-stakes reversible trials, or in teams with low psychological safety (will become political attack).

Use when a decision will trigger downstream reactions — pricing changes, policy design, product trade-offs, organizational moves, platform rule changes, investment choices. Triggers on Chinese phrases 涨价 / 改规则 / 调架构 / 投资 / 平台政策 / 算法调整 / 长期 vs 短期, and English signals "let's just X", "this is the obvious move", "we should change Y". Especially when intuition says answer is "obvious" (often signals 1st-order best = 2nd-order worst), or when user describes a market/users/employees who WILL react to the decision. Howard Marks's investing edge. Do NOT use for decisions with no feedback loop (personal consumption), time-critical incidents (use ooda-loop), or already-paralyzed analysis (cap recursion at 3-4 orders).

Use when user is in an emotionally-charged moment (anger, excitement, FOMO, urgency, regret-anticipation) and about to make a decision they may regret. Triggers on Chinese phrases 想发出去 / 想立刻 yes / 冲动 / 来不及了 / 现在不做就 / 火大 / 受不了 / 一定要 / 必须现在, and English signals "I'm about to send", "I want to quit right now", "this is killing me". Use BEFORE the action, not after. Especially when user wavers between short-term gratification and long-term value (food, money, anger replies, impulsive job changes, heated conversations). Asks how 10 minutes / 10 months / 10 years later will feel — exposes which time-window's emotion is hijacking judgment. Do NOT use for true emergencies (use ooda-loop), already-irreversible decisions (do acceptance work instead), purely rational choices, or as a procrastination excuse (asking 10/10/10 for 1 hour means it's no longer the right tool).

Use when user faces a binary or two-way decision they've been agonizing over — career changes, hiring, tech stack choices, product bets, life pivots, or any high-stakes call where short-term emotion likely clouds judgment. Triggers on Chinese phrases 要不要 / 该选 X 还是 Y / 纠结 / 拿不准 / 想了好几天 / 在 X 和 Y 之间. Also trigger proactively when user describes a decision accompanied by strong feelings (excitement, dread, urgency, FOMO), confirmation-bias signals ("我心里其实有答案了"), or when they've explicitly compared only 2 options. Do NOT use for time-critical decisions (use ooda-loop), reversible low-cost trial decisions (just try), or Cynefin Clear-domain SOPs.

INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills.

INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

INVOKE THIS SKILL for LangGraph workflows, parallel execution, interrupts, or streaming. Covers Send API for fan-out, interrupt() for human-in-the-loop, Command for resuming, and stream modes (values/updates/messages).

INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph creation, node functions, edges, state schemas with reducers (Annotated), and the Command API.

INVOKE THIS SKILL when your LangGraph needs to remember state across calls, use memory, or persist conversations. Covers checkpointers (MemorySaver, Postgres), thread_id configuration, and Store for long-term memory.

Automated daily planning and reflection system with morning briefs, wind-down prompts, sleep nudges, and weekly reviews. Use when the user wants to set up a structured daily routine, morning briefings, evening reflection prompts, or weekly planning sessions. Triggers include requests for daily schedules, morning briefs, wind-down routines, sleep reminders, weekly reviews, productivity systems, or daily planning automation.

AI language tutor for learning ANY language through conversation, vocab drills, grammar lessons, flashcards, and immersive practice. Use when the user wants to: learn a new language, practice vocabulary, study grammar, do flashcard drills, translate phrases, practice conversation, prepare for travel, learn slang/idioms, or improve pronunciation. Supports ALL languages including Spanish, French, German, Japanese, Chinese (Mandarin/Cantonese), Korean, Arabic, Hindi, Bengali/Bangla, Portuguese, Russian, Italian, Turkish, Vietnamese, Thai, Swahili, Hebrew, Polish, Dutch, Greek, and 100+ more.

Generate an energy-optimized, time-blocked daily plan

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Now with automatic session recovery after /clear.

Meta-cognitive self-learning system - Automated skill evolution based on predictive coding and value-driven mechanisms.

AI-powered job search assistant. Scan job listings, track applications, maintain dream company lists, and generate tailored resumes and cover letters. Use when the user wants to find jobs, run a job scan, set up job search, track applications, or prepare application materials.

Generate professional resumes that conform to the Reactive Resume schema. Use when the user wants to create, build, or generate a resume through conversational AI, or asks about resume structure, sections, or content. This skill guides the agent to ask clarifying questions, avoid hallucination, and produce valid JSON output for https://rxresu.me.

Professional resume builder with PDF export, ATS optimization, and analysis capabilities. Use when users need to (1) Create new resumes from scratch, (2) Customize/tailor existing resumes for specific roles, (3) Analyze resumes and provide improvement recommendations, (4) Convert resumes to ATS-friendly PDF format. Supports chronological, functional, and combination resume formats.

遵循 Robert C. Martin 的《代码整洁之道》原则，进行代码审查、重构和编写。涵盖命名、函数、注释和错误处理的最佳实践。

Java COLA 架构指南 - 阿里巴巴 COLA 整洁分层架构框架。用于 Java/Spring Boot 项目的 DDD 分层架构设计、代码结构规范、应用架构重构。触发条件：COLA、Java DDD、Spring Boot 架构、分层架构、整洁架构、六边形架构、洋葱圈架构、代码分层、模块划分、Gateway 模式、Repository 模式、CQRS、架构重构、职责分离、Maven 多模块。创建 Java 文件时自动应用 COLA 目录结构和命名规范。

Python COLA 架构指南 - 将阿里巴巴 COLA 整洁分层架构适配到 Python/Flask/FastAPI 项目。用于 Python 后端的 DDD 分层架构设计、代码结构规范、应用架构重构。触发条件：Python DDD、Flask 架构、FastAPI 架构、Python 分层架构、整洁架构、六边形架构、洋葱圈架构、COLA、代码分层、模块划分、Gateway 模式、Repository 模式、CQRS、架构重构、职责分离。创建 Python 文件时自动应用 COLA 目录结构和命名规范。

领域驱动设计（DDD）建模与架构指导。用于创建领域模型、设计限界上下文、实现聚合根、实体、值对象、领域服务和领域事件。支持战略设计（上下文映射、子域划分）和战术设计（聚合、仓储、工厂模式）。当用户需要进行领域建模、微服务划分、复杂业务逻辑设计时使用。

Applies principles from Robert C. Martin's 'Clean Code'. Use this skill when writing, reviewing, or refactoring code to ensure high quality, readability, and maintainability. Covers naming, functions, comments, error handling, and class design.

Format commit messages using the Conventional Commits specification. Use when creating commits, writing commit messages, or when the user mentions commits, git commits, or commit messages. Ensures commits follow the standard format for automated tooling, changelog generation, and semantic versioning.

AI-powered git commit message generation. Uses LLM to summarize changes and create meaningful commit messages. Triggers when user wants to commit changes, amend/squash commits, or needs LLM to summarize changes for commit messages. Chinese triggers: 提交, 提交代码, 提交更改, 提交修改, 要提交, commit, 提交信息, git 提交, 生成提交信息.